Method and apparatus for a model assessing debtor behavior

ABSTRACT

A computer implemented method for assessing different expected payment behavior of a debtor with respect to different creditors.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 60/782,934 filed Mar. 16, 2006 which isincorporated herein by reference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF THE INVENTION

The present invention relates generally to forecasting systems thatmodel payment behavior of debtors with respect to creditors.

BACKGROUND OF THE INVENTION

For some time companies have been using statistical-based modeling toassess the risk of payment inherent in doing business with potential andpresent customers. Typical of this methodology is the use of creditinformation gleaned from one of the major credit bureaus to assesswhether an individual or business entity with whom the company iscontemplating doing business has a record of appropriate payment. Thus,based on past history, an assessment of a future likelihood ofappropriate slow or non-payment may be made. This past history may alsoinclude response to collections efforts and the like.

SUMMARY OF THE INVENTION

Various deficiencies in the prior art are addressed through theinvention of a computer implemented method and apparatus for modelingthe behavior of a debtor to enable determining a probability of a Creditand/or Collection Event (“CCE”), the financial consequences of which maythen be evaluated. The CCE may be a debtor going into bankruptcy, anexperience of a charge off with one or more creditors, a severedelinquency in meeting payment obligations and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present invention can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 depicts a high-level block diagram of a computer implementedapparatus according to an embodiment of the invention;

FIG. 2 depicts a flow diagram of a method for developing a businesscredit data interchange (BCDI) score;

FIG. 3 depicts a flow diagram of a method for implementing a BCDI score;

FIG. 4 depicts a flow diagram of an alternate method for implementing aBCDI score; and

FIG. 5 depicts a high-level block diagram of a general-purpose computersuitable for use in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Business Credit Data Interchange (“BCDI”) groups have been created toprovide an information sharing mechanism. In the BCDI, companies sharetheir respective accounts receivable performance data on theircustomers, and the BCDI management group manages and summarizes thisdata. The summarized data from the BCDI can be used to generate aGeneric Creditor Model (see Equation 1 in section below) to assess thelikelihood of a future Credit and/or Collection Event. Examples of a CCEare described in more detail below with respect to FIG. 2. TheProbability of a Credit and/or Collection Event (“P(CCE)”) of debtor (i)for creditor (j) takes the following functional form:P(CCE_(iJ))=F(BCDI_(iJ))Where P(CCE_(iJ))=P(CCE_(i1))=P(CCE_(i2))= . . . P(CCE_(iM))And BCDI_(iJ)=ΣBCDI_(ij) over all j

The above equation is read as P(CCE) of debtor (i) with any creditor (j)is a function of the aggregated BCDI data elements (“BCDI_(iJ)”) fordebtor (i) across all M creditors in the BCDI. P(CCE_(ij)) uses J as thesecond subscript because P(CCE) does not vary by creditor. P(CCE_(ij))means the P(CCE) of debtor (i) with respect to creditor (j). When themodel is being built, the data elements are drawn from an archiveconsistent with the start of the performance period of the CCE. When themodel is implemented, the data elements are captured at time of score.The use of the symbol Σ in the term ΣBCDI_(ij) is meant in the broadersense of aggregation rather than just a literal sum of data elements.

While this technique provides some indication of likely debtor actions,the technique is insufficiently accurate. Many debtors under thistechnique generate a low P(CCE) but still end up in a bankruptcy,nonpayment, slow payment or some other negative scenario. First andforemost the P(CCE) of debtor (i) does not differ by creditor. TheP(CCE) is the same whether we are looking at a P(CCE) of debtor (i) forcreditor (j) or creditor (k). However, the timeliness of debtor (i) inmeeting its payment obligations can certainly differ by whether debtor(i) is making a payment decision for creditor (j) or creditor (k).Perhaps the debtor can pay one of these creditors on time but the otherwill be paid late (or not all). Second, the data relationships from theBCDI database to predict P(CCE_(iJ)) are aggregate relationships with nodifference accounted for in the relationship of any data from creditor(j) to the P(CCE_(iJ)) for debtor (i). For example, creditor (j) mayusually be paid on time or with slight delay while other creditors mayconsistently receive late payments or no payments. If creditor (j)incurs a late payment from debtor (i), this change in behavior may bemore significant than the incurring of a late payment by one of theother creditors. Third, the financial consequences of the CCE are notreadily apparent. Thus, the present BCDI P(CCE) technique is limited inwhat information it provides and what is provided does not adequatelypredict some very critical behaviors associated with debtors.

A variation of the technique described above assigns a score rather thana probability. It has the property that the score for debtor (i) withrespect to creditor (j) is the same as the score with respect tocreditor (k). This variation has additional limitations in that theP(CCE) is not readily apparent from the score and the determination ofsuch a probability may be impossible or involve additional labor.

The typically summarized and/or aggregated data from the BCDI, inconjunction with data drawn from creditor (j), can be used to createCustom Creditor Models:P(CCE_(ij))=F _(j)(BCDI_(iJ)) orP(CCE_(ij))=F _(j)(AR_(ij),APP_(ij),OTHER_(i)) orP(CCE_(ij))=F _(j)(BCDI_(iJ),AR_(ij),APP_(ij),OTHER_(i))

These custom models are discussed in more detail below with respect toequations 3, 5 and 7.

The invention will be described within the context of a methodology formodeling debtor behavior to determine the P(CCE) for debtor (i) withcreditor (j). That is, a probability that the debtor will fail to pay aparticular creditor and, more specifically, that the debtorpreferentially pays one creditor before another creditor. In this mannera creditor may manage the risk associated with future transactions withthe debtor. A first application of the invention is the modeling ofdebtor behavior to determine the P(CCE). A second application of theinvention is the modeling of debtor behavior to rank debtors accordingto a likelihood of repayment after one or more P(CCE)s, and to furtherrank the debtors according to the amount of payment likely to bereceived. Creditors are “owners” of consumer debt, commercial debt,medical/patient debt, leases, trade credit, revolving debt, installmentdebt, among others type of debt or, generally speaking, credittransactions.

Various embodiments of the invention provide a method for modelingdebtor behavior, comprising: obtaining historic debtor data, creditordata, and debtor/creditor data found in “Business Credit DataInterchange” (BCDI) groups and/or possibly other data sources regardingdebtor (i) outside of the BCDI; determining a P(CCE_(ij)) model byprocessing the historic debtor data, creditor data, and debtor/creditordata according to either of (1) a generalized linear modeling techniquewith a link function based upon a Generalized Beta of the Second Kind(GB2) family of distributions or (2) a generalized linear modelingtechnique using a link function based upon a member of the G-and-Hfamily of distributions; and storing, in a memory, values correspondingto the P(CCE_(ij)).

One embodiment includes determining an expected conditional Utility Lossmodel by processing the historic debtor data, creditor data, anddebtor/creditor data found in Business Credit Data Interchange (“BCDI”)groups and/or possibly other data sources regarding debtor (i) outsideof the BCDI according to either of (1) a generalized linear modelingtechnique using a member of the natural exponential family or (2) amaximum likelihood estimation fit to a member of the GB2; and storing,in the memory, values corresponding to the expected conditional UtilityLoss model.

Another embodiment includes determining an Expected Utility Loss (“EUL”)for debtor (i) with creditor (j) using the P(CCE_(ij)) and the expectedconditional Utility Loss at time of P(CCE_(ij)) calculation.

A first algorithm (“Algorithm 1”) associated with an embodiment of theevaluation engine utilizes a generalized linear modeling technique witha link function that is an inverse Cumulative Density Function from theGeneralized Beta of the Second Kind family of distributions to provide amaximum likelihood estimation of the P(CCE) for debtor (i) with creditor(j) and generates thereby the first output data set. This four parameterdistributional family includes logistic regression as a specialized formof this technique. Logistic regression usually works well, where workingwell refers to the maximum likelihood statistic of the model penalizedfor complexity with a criterion such as the Schwartz Bayes Criterion.The algorithm also examines link functions that are an inverseCumulative Density Function from the G-and-H family of distributions.The G-and-H family (also a 4 parameter distributional family) includesprobit regression as a specialized form of the technique. Probitregression usually works well (as defined above) within the G-and-Hfamily, however, logistic regression is usually superior to probitregression in terms of the maximum likelihood statistic of the model.For this algorithm the dependent variable is dichotomous with 0indicating non payment and 1 indicating payment. This algorithm usescredit history information from the second input data set for eachdebtor and applies the extracted information to the credit events of therespective debtors included within the BCDI. Thus credit and/or BCDIdata determines a P(CCE) that is used to determine the relative creditworthiness of the debtors and to compare across creditors. The P(CCE) isused to rank the debtor population within the debtor portfolio andacross creditors and provide thereby the first output score.Alternatively, one or more of a neural network processing technique, alinear regression technique, a discriminant analysis technique and arandom forests technique as well as other techniques) may be used togenerate the first output data set.

A second algorithm (“Algorithm 2”) associated with an embodiment of theevaluation engine utilizes a general linear model to provide a maximumlikelihood estimation of the Utility Loss conditional that a CCE hasoccurred. Alternatively a generalized linear modeling technique using amember of the natural exponential family such as Normal, Poisson, Gamma,Inverse Gaussian, Negative Binomial, Logarithmic, and CompoundPoisson/Gamma and/or other distributions is used. The Gamma distributionusually works well (as defined above) within the natural exponentialfamily; however, the general linear model regression is usually superiorto this distribution in terms of the maximum likelihood statistic of themodel. The model also fits distributions from the Generalized Beta ofthe Second Kind family of distributions using Maximum LikelihoodEstimation. The Generalized Beta of the Second Kind family (a 4parameter distributional family) includes Burr III, Weibull, Lognormaland Standard Beta distributions as specialized forms. The Standard Betatends to work well (as defined above) within the Generalized Beta of theSecond Kind family; however, the general linear model regression isusually superior to this distribution in terms of the maximum likelihoodstatistic of the model. For this algorithm the dependent variable is theUtility Loss conditional that a CCE has occurred. This algorithm usescredit history information from the second input data set for eachdebtor and applies the extracted information to the credit events of therespective debtors included within the BCDI. Alternatively, neuralnetwork techniques, discriminant analysis, random forests and othertechniques may be used to generate an estimate for the sum of paymentsconditional that payments have been made.

For each debtor (i) and creditor (j) in the first dataset, the ExpectedUtility Loss is the product of the P(CCE) from the first algorithm withthe above estimate for the conditional Utility Loss:Expected Utility Loss(e.g., Dollars)=(Probability of CCE)×(ExpectedConditional Utility Loss).

Software instructions defining a method for modeling debtor behavioraccording to the invention may be implemented by a computer or stored ona computer readable medium, wherein the method comprises obtaininghistoric customer placement data for each of at least one debtor in adebt portfolio; obtaining historic credit data for each of the at leastone debtor in the debt portfolio; determining a P(CCE) model byprocessing the historic customer placement data and historic credit dataaccording to either of (1) a generalized linear modeling technique witha link function based upon a Generalized Beta of the Second Kind (GB2)family of distributions or (2) a generalized linear modeling techniqueusing a link function based upon a member of the G-and-H family ofdistributions; and storing, in a memory, values corresponding to theP(CCE) model.

Thus credit and/or placement data determines an EUL that is used todetermine the relative credit worthiness of the debtors and to compareacross creditors. The EUL is used to rank the debtor population withinthe debtor portfolio and across creditors and provide thereby the secondoutput score.

Alternatively, the EUL is estimated directly by applying a combinationof the first and second algorithms to the dependent variable UtilityLoss (rather than the conditional Utility Loss). Such a procedureapplies these algorithms in the context of a Tobit model withsignificant censorship at 0. That is, the EUL may also be directlyestimated using a Tobit model with or without the P(CCE) model (asdiscussed herein) as an input to the Tobit model.

There are numerous uses for the P(CCE_(ij))s and/or EULs and comparisonsinformation provided according to the invention including:

-   -   1. Creditors can use this release orders with existing customers        or mitigate its risk before releasing such order.    -   2. Creditors can manage credit line by increasing low risk        customers and decreasing high risk customers.    -   3. Prioritization of risk based collection activities based on        the probabilities of delinquency.    -   4. Creditors can use the probabilities to change the contractual        terms of the lending relationship in regards to interest rates,        fees, period of payment, and length of contract term, among        other provisions.

In addition to the above, new business credit decisions can be madeexamining the individual scores by creditor or average across creditors.For example, suppose the average score of a debtor is 84 (where higheris better) in a BCDI composed of 4 creditors: 78 for creditor 1, 80 forcreditor 2, 82 for creditor 3, and 96 for creditor 4 (the use of asimple average is for demonstration. Other types of averages, such asbalance weighted, may be used if appropriate).

Generally speaking, the present invention utilizes a database andstatistical model specification that determines the following:

(1) Probability of a CCE (“P(CCE_(ij))”) for debtor (i) with specificcreditor (j); (2) The expected utility loss of a CCE for debtor (i) withspecific creditor (j), and (3) a ranking of creditors in the BCDIassociated (or potentially associated) with the debtor according tolikelihood of payment.

For a possible CCE, the event probability is bounded by a confidenceinterval determined by the standard error of that CCE. The CCEprobability along with trend data associated with the CCE is used toprovide an estimation of the range and trend of the CCE. In variousembodiments of the invention, reports are generated to facilitate thepresentation of the results generated using the invention.

One embodiment of the invention comprises a method for modeling theP(CCE_(ij)) behavior of a debtor (i) with respect to a creditor (j), themethod comprising aggregating debtor account receivable or payment datafrom each of a plurality of creditors, and using the aggregated debtoraccount receivable or payment data and debtor payment data from thecreditor (j) to determine P(CCE_(ij)). That is, the creditor (j) modelsthe behavior of a debtor using aggregated data from other creditorsassociated with the debtor as well as the creditor's own data associatedwith the debtor. The general functional form is as follows:P(CCE_(ij))=F(AR_(ij),APP_(ij),OTHER_(i),BCDI_(i1),BCDI_(i2) . . .,BCDI_(iM),BCDI_(iJ))

The above equation is read as P(CCE) for debtor (i) with creditor (j),is a function of account receivable, AR, data for debtor (i) withcreditor (j), other optional internal data, APP, for debtor (i) withcreditor (j), other optional third party data, OTHER, providing moreinformation on debtor (i), groups of data elements specifically tied toeach creditor in the BCDI, and the aggregated BCDI data elements(“BCDI_(iJ)”) for debtor (i) across all M creditors in the BCDI.

Based upon this functional form the P(CCE_(ij)) is determined by datarelationships specifically between debtor (i) and creditor (j) as wellas data relationships between debtor (i) and other creditors. Thisability to allow the data relationships to differ by creditor, all elseequal, can only increase the accuracy of P(CCE_(ij)).

Prior art generally uses a summarized BCDI database. The above form goesbeyond using summarized and/or aggregated data because the data elementsare identified according to the creditor from which they originated.

Comparisons across creditors use a specialized functional form:P(CCE_(ij))=F(OTHER_(i),BCDI_(i1),BCDI_(i2), . . .,BCDI_(iM),BCDI_(iJ)),

Alternatively, P(CCE_(ij))=F(BCDI_(ij). OTHER_(i), BCDI_(i1), BCDI_(i2),. . . BCDI_(iM), BCDI_(iJ)),

where the term for creditor (j) is listed first and only once.

This functional form includes AR and APP only to the extent that thisdata is shared in the BCDI. This functional form enables the reportingto creditor (j) of the P(CCE) for debtor (i) with creditor (j) and alsothe P(CCE_(ij)) for all other J−1 creditors, the average P(CCE) acrosscreditors and so on. In this way creditor (j) can accurately know theP(CCE) of debtor (i) with creditor (j), and also the payment risk ofdebtor (i) with others. Note that the P(CCE_(ij)) from the first formcould differ from the second form if creditor (j) has proprietary datathat affect the probability of a CCE. Because members of a BCDI areusually encouraged and/or required to share meaningful credit data, thespecialized functional form is typically used. Thus, the specializedform will be assumed without loss of generality throughout this patentapplication.

The expected utility loss of a CCE for debtor (i) with specific creditor(j) can be used to evaluate the financial consequences of a CCE:EUL_(ij)=P(CCE_(ij))*UL_(ij)

The above equation is read as Expected Utility Loss (“EUL”) is theproduct of the P(CCE) for debtor (i) with creditor (j) and expectedconditional Utility Loss (“UL”) for creditor (j) of that CCE with debtor(i). The UL is a function of variables, such as balance outstanding, andmay include monetary costs associated with the incurring of a CCE, suchas collection efforts. The UL may also include non monetary orintangible considerations such as distraction of management time orinvestor loss of confidence in the management of creditor (j). Relevantcollateral may be considered as a reduction to the UL.

For example, if the CCE is 30 day delinquency and the UL is writeoffdollars, then UL=k*(amount of delinquency) is a type of UL function,where k is the average proportion of resulting writeoff dollars to 30day delinquency dollars.

A particularly useful form of EUL is referred to as Payment At Risk(“PAR”):PAR_(ij) =P(CCE_(ij))*Balance_(ij)

The above equation is read as PAR is the product of the Probability of aCCE for debtor (i) with creditor (j) and the balance outstanding asshown in the BCDI at time of estimation of P(CCE_(ij)). This form hasthe form of a weighted delinquency measure where the weights reflect thefinancial size of the relationship between debtor (i) and creditor (j).The balance outstanding is usually the sum of the missed payments fromdebtor (i) to creditor (j) but it could also include any remainingcontractual payments (for example, missed lease payments as well as theremaining lease payments).

Because PAR is particularly useful and simple, PAR will be used in placeof EUL without loss of generality throughout this patent application.Dollars At Risk (“DAR”) is synonymous with PAR and is usually used fordollar based entities.

The above UL examples are special cases of the more general situationwhere the UL outcome is modeled using the algorithms discussed below.For example, if the CCE is 30 day delinquency and the UL is writeoffdollars (for example, 180 day delinquency), the process would performbivariate analysis to build a model for writeoff dollars conditionalupon the occurrence of a 30 day delinquency.

A ranking of creditors in the BCDI associated with the debtor accordingto likelihood of payment follows naturally from the calculation ofP(CCE_(ij)). Potential creditors that are members of the BCDI canevaluate their potential ranking in the list of creditors by evaluatingthe P(CCE_(ij)) of the proposed relationship in the context of theexisting relationships of debtor (i). Such rankings can be determinedconditionally based upon the occurrence of one or more P(CCE_(ij))involving one or more creditors. A ranking of creditors can also be doneaccording to EUL or PAR.

Thus, generally speaking, the invention helps determine a likelihood ofpayment not being made in a timely manner by debtor (i) with creditor(j), and the amount of payment under risk, as well as payments not beingmade in a timely manner with other creditors. Such information has manyimportant uses, some but not all uses are:

-   -   Deciding to release incremental orders to customers    -   Adjusting (increase/decrease) Credit Lines    -   Prioritizing the work of collectors    -   Optimizing terms of contract; days payment due, interest, length        of contract    -   Soliciting business from new customers

The invention uses historical data found in the BCDI and createsalgorithms for P(CCE_(ij)). The algorithms are stored in memory andsubsequently may be used to process current and historical data found inthe BCDI to update P(CCE_(ij)). The historical data and algorithms arediscussed below.

FIG. 1 depicts a high-level block diagram of functional elementsassociated with an embodiment of the invention. Specifically, FIG. 1depicts a BCDI Data Source, illustratively a debtor/creditor portfolio110 including debtor information 112 and/or creditor information 114and/or debtor/creditor information 116 (i.e., information that links thedebtor/creditor transactions) that provides portfolio information,debtor information, creditor information, and debtor/creditorinformation to an evaluation system 120. This information comprises afirst data set for processing by the evaluation system 120.Additionally, a Credit Information Data Source 118 provides a seconddata set for processing by the evaluation system 120. The CreditInformation Data Source 118 comprises, illustratively, a commercial orprivate source of credit information pertaining to each debtor in theCustomer Placement Data Source 110. Other databases (not shown) such asdemographic databases may be used to provide information to theevaluation system 120, as discussed below.

The evaluation system 120 comprises an evaluation engine 122 whichprocesses the two received data sets according to, illustratively, anyof a plurality of algorithms provided by a methodology selection storageunit 124. The output of the evaluation engine 122 is provided to areport generator 126 for providing a machine readable or human readablereport. Several exemplary reports will be discussed below with respectto FIG. 5. Optionally, a result processor 128 performs furtherprocessing of the evaluation engine output to derive additionalinformation, such as comparisons of a present portfolio, debtor,creditor, or debtor/creditor to a previously processed portfolio,debtor, creditor, or debtor/creditor.

The evaluation system 120 is depicted in FIG. 1 as being controlled by acontroller/client device 130, illustratively a general purpose computer.The controller 130 operates to, illustratively, cause specificalgorithms to be selected by the evaluation engine, cause specificreports to be generated by the report generator 126, cause specificpost-processing operations to be performed by the result processor 128and so on. The controller 130 operates to receive information from anyof the functional elements depicted in FIG. 1.

Each of the functional elements depicted in FIG. 1 may be implemented asone or more computing devices such as, for example, described in moredetail below with respect to FIG. 4. Generally speaking, the functionalelements of FIG. 1 may be combined in any way within the context of oneor more general purpose or special computing devices to implements thevarious functions associated with the present invention, as describedherein.

While generally described as processing BCDI data in combination withCredit Information Data data, various embodiments of the invention areimplemented by processing one of both of BCDI data and CreditInformation Data data. Moreover, demographic data and other data mayalso be processed in combination with either or both of the customerplacement data and credit history data.

The evaluation engine 122 is adapted to evaluate a portfolio of debtorsor an individual debtor. A debt portfolio typically comprises accountsreceivable (A/R) that have been deemed by one or more creditors to beuncollectible or not worth collecting because the accounts have reacheda status of severe delinquency status or write-off. Debt portfolioscomprised of slightly delinquent accounts or even current accounts canbe evaluated as well. Debt portfolios may be produced by, for example, acompany or group of companies that would like to get some return ontheir uncollected A/R but may not have the in-house collectionsexpertise to generate a sufficient return. Alternatively, a company mayuse portfolio sales to manage their balance sheet. The portfolio isoffered for sale to a debt buyer, typically at a discount to the facevalue of the uncollected A/R. The debt buyer should estimate the valueof the portfolio. More specifically, the debt buyer should estimate howmuch of the uncollected A/R may be ultimately collected and the likelycost to the debt buyer of realizing those collections.

Modeling Debtor Behavior Before a Credit or Collection Event

A first application of the invention is the modeling of debtor behaviorto determine the P(CCE) and/or EUL for debtor (i) with creditor (j).

Business Credit Data Interchange (“BCDI”) groups have been created toprovide an information sharing mechanism between creditors. In the BCDI,companies share their accounts receivable performance data on theircustomers and the BCDI management group will manage and summarize suchdata. BCDI data within the context of the present invention comprisesone or more variables such as those presented in the below list. It isnoted that some of the variables are historic in nature and some arepresent observations.

BCDI Variables

The following is a non-exclusive listing of BCDI variables that may beused in algorithms according various embodiments of the invention:

1. Total Outstanding Balance across creditors;

2. Total Credit Line across creditors;

3. Amount current across creditors;

4. Amount 1-30 days past due across creditors;

5. Amount 31-60 days past due across creditors;

6. Amount 61-90 days past due across creditors;

7. Amount 91-120 days past due across creditors;

8. Amount 121-150 days past due across creditors;

9. Amount 151-180 days past due across creditors;

10. Amount 181-210 days past due across creditors, etc;

11. Amount of write-off (loss) across creditors;

12. Amount placed for collection across creditors;

13. Average days past due across creditors;

14. Original (first) trade credit/lease/loan amount across creditors;

15. Unapplied cash amount across creditors;

16. Late-fee amount charged across creditors;

17. NSF check amount or indicator across creditors;

18. Indicator or date of bankruptcy across creditors;

19. Placed for collection amount across creditors;

20. Term of trade credit/lease/loan across creditors;

21. Date of first lease/loan across creditors; and

22. Tenure in months or date became customer across creditors.

In addition to the above, combinations of variables, analyses acrosstime, ratios, logs, truncations, censoring, minimums, maximums anddifferences among other transformations are appropriate to use invarious embodiments of the invention. For example, the BCDI variablesoptionally include (or are associated with) demographic valuespertaining to debtors.

Accounts Receivable Variables

The following is a non-exclusive listing of accounts receivablevariables that may be used in algorithms according various embodimentsof the invention:

1. Total Outstanding Balance;

2. Total Credit Line;

3. Amount current;

4. Amount 1-30 days past due;

5. Amount 31-60 days past due;

6. Amount 61-90 days past due;

7. Amount 91-120 days past due;

8. Amount 121-150 days past due;

9. Amount 151-180 days past due;

10. Amount 181-210 days past due, etc.;

11. Amount of write-off;

12. Amount placed for collection;

13. Average days past due;

14. Original (first) trade credit/lease/loan amount;

15. Unapplied cash amount;

16. Late-fee amount charged;

17. NSF check amount or indicator;

18. Indicator or date of bankruptcy;

19. Placed for collection amount;

20. Term of trade credit/lease/loan;

21. Date of first lease/loan; and

22. Tenure in months or date became customer.

In addition to the above, combinations of variables, analyses acrosstime, ratios, logs, truncations, censoring, variances, averages,measures of volatilities, minimums, maximums, differences among othertransformations and statistical calculations are appropriate to use invarious embodiments of the invention.

With respect to the above variable sets, bureau data, demographic dataand other third party data may be used. Such data is usually suppliedthrough layouts specified by the source entity.

The following are three applications of credit or collection scoremethodologies that can be applied to one or both of the above datavariable sets:

I. Generic Creditor Model:

For the Generic Creditor Model (“GCM”), P(CCE) for debtor (i) withcreditor (j) takes the following functional form:P(CCE_(ij))=F(BCDI_(ij))  (equation 1)Where P(CCE_(iJ))=P(CCE_(i1))=P(CCE_(i2))= . . . P(CCE_(iM))And BCDI_(iJ)=ΣBCDI_(ij) over all jPAR_(iJ) =P(CCE_(iJ))  (equation 2)

The above equation 1 is read as the P(CCE) for debtor (i) with anycreditor (j) is a function of the aggregated BCDI data elements(“BCDI_(iJ)”) for debtor (i) across all M creditors in the BCDI.P(CCE_(iJ)) uses J as the second subscript because P(CCE) does not varyby creditor. P(CCE_(ij)) means the P(CCE) of debtor (i) with respect tocreditor (j). The generic creditor model P(CCE) for debtor (i) does notdiffer by what creditor is being provided the P(CCE). The above equation2 is read as PAR is the Probability of a CCE for debtor (i) with anycreditor (j). Note that the generic creditor model does not evaluate thepossibly differing financial consequences of a CCE by creditor.

II. Custom Creditor Model:

The GCM can be customized to an individual creditor (j) to create aCustom Creditor Model (“CCM”). The creditor creates the model andapplies the model. The details and results of the model are notcommunicated to other creditors. Typically the creditor (j) receivesdata from a BCDI and uses the data in conjunction with its own internaldata and/or data from third party sources. If the creditor participatesin the BCDI some or all of the internal data may be included as part ofthe aggregated BCDI data elements. Upon request the BCDI may provideaggregated BCDI data elements such that data from the creditor is notpart of the aggregated BCDI data elements. Removal of the creditor (j)data from the aggregated BCDI data elements is preferred but notnecessary.

Thus, in one embodiment of the invention, each of a plurality of groupswithin a single BCDI (or plurality of groups associated with respectiveBCDIs) provides a data set to a creditor. The creditor is a member ofeach of the BCDI groups. The creditor utilizes its own custom creditormodel to process the multiple BCDI group data sets to determine a P(CCE)for a particular debtor with respect to the creditor, to determine aP(CCE) for the particular debtor with respect to one or more othercreditors, and/or to determine a likely order or sequence of CCEs thatthe debtor will impose upon the creditors. The sequence of CCEs may bedetermined by ranking, in order of likelihood, the P(CCE) for theparticular debtor with respect to each of the one or more creditors.

An important change from the GCM is that data from creditor (j) withrespect to debtor (i) is now specifically identified as coming fromcreditor (j) rather than just being part of the aggregate data elements.Another change is that the CCE (the dependent variable) is calculatedusing data from creditor (j) although a hybrid dependent variable thatcombines creditor data with BCDI aggregate data elements and/or a thirdparty data source may also be used. For example, 91+ day delinquencywith creditor (j) or a severe delinquency in the BCDI or a bankruptcyfiling recorded by a bureau.

In the CCM models that follow it is understood that creditor (j)typically performs calculations only for itself, i.e., j is the onlycreditor for whom the model is built and the results computed. Thevalues of the aggregated BCDI data elements are known but the creditorof origin for a particular transaction is not known. Each member of theBCDI may develop its own unique model but these development efforts arenot shared. Typically the BCDI is not involved with the calculation ofP(CCE_(ij)).

In the CCM, the P(CCE) of debtor (i) for creditor (j) takes thefollowing functional forms:

A. CCM using BCDI data only:P(CCE_(ij))=F _(j)(BCDI_(j))  (equation 3)PAR_(ij) =P(CCE_(ij))  (equation 4)

The above equation 3 is read as P(CCE) for debtor (i) with the creditor(j) is a function of the aggregated BCDI data elements (“BCDI_(iJ)”) fordebtor (i) across all M creditors in the BCDI. The above equation 4 isread as PAR is the Probability of a CCE for debtor (i) with the creditor(j). Note that CCM does not evaluate the financial consequences of aCCE.

This form uses the same aggregated BCDI data elements as the GCM,however, the prediction function F_(j) is specific to the creditor (j).For example, the prediction function could be a result of using theaggregated BCDI data elements to predict a CCE (the dependent variablein the modeling process) based upon performance data from the creditor(j).

B. CCM using the creditor's internal accounts receivable and other dataonly:P(CCE_(ij))=F _(j)(AR_(ij),APP_(ij),OTHER_(i))  (equation 5)PAR_(ij) =P(CCE_(ij))  (equation 6)

The above equation 5 is read as P(CCE) for debtor (i) with the creditor(j) is a function of accounts receivable data (“AR”) for debtor (i) withthe creditor (j) other optional internal data (“APP”) for debtor (i)with creditor j and other optional third party data (“OTHER”) providingmore information on debtor (i). The above equation 6 is read as PAR isthe Probability of a CCE for debtor (i) with the creditor (j). Anexample of OTHER data is data from a credit bureau, a demographic datasource, a private data source and the like. Although the AR_(ij) may becomposed of data that is sent to the BCDI, the source of the AR_(ij) isthe creditor (j).

C. CCM using the creditor's internal accounts receivable, other data,and with BCDI data only:P(CCE_(ij))=F _(j)(BCDI_(iJ),AR_(ij),APP_(ij),OTHER_(i))  (equation 7)PAR_(ij) =P(CCE_(ij))  (equation 8)

The above equation 7 is read as P(CCE) for debtor (i) with the creditor(j), is a function of the aggregated BCDI data elements (“BCDI_(iJ)”)for debtor (i) across all M creditors in the BCDI, AR data for debtor(i) with the creditor (j), APP data for debtor (i) with creditor (j),and OTHER data on debtor (i). The above equation 8 is read as PAR is theProbability of a CCE for debtor (i) with the creditor (j). Although theAR_(ij) may be composed of data that is sent to the BCDI, the source ofthe AR_(ij) is the creditor (j).

III. Business Credit Data Interchange Score:

In one embodiment of the invention, the BCDI score provides a functionalform that scores by debtor (i) and creditor (j), using the creditors'own data as members of the BCDI in conjunction with the aggregated BCDIdata elements (“BCDI_(ij)”), in the following form:P(CCE_(ij))=F(OTHER_(i),BCDI_(i1),BCDI_(i2), . . .,BCDI_(iM),BCDI_(iJ))  (equation 9)Alternatively, P(CCE_(ij))=F(BCDI_(ij),OTHER_(i),BCDI_(i1),BCDI_(i2), .. . BCDI_(iM),BCDI_(iJ)),  (equation 10)

-   -   where the term for creditor (j) is listed first and only once.        PAR_(ij) =P(CCE_(ij))*Balance_(ij)  (equation 11)

The above equations 9/10 are read as Probability of a CCE for debtor (i)with creditor (j) is function of data contained in BCDI covering debtor(i) with creditor (j), other optional third party data, OTHER, providingmore information on debtor (i) and data elements for debtor (i) acrossall j creditors in the BCDI. The above equation 11 is read as PAR is theproduct of the Probability of a CCE for debtor (i) with creditor (j) andthe balance outstanding as shown in the BCDI at time of estimation ofP(CCE_(ij)).

Thus the BCDI score allows a time series cross sectional model across amatrix of creditors and debtors. Based upon this functional form,calculations are made for the P(CCE) for debtor (i) for the creditor (j)and report to creditor k) the P(CCE) for all other j−1 creditors, theaverage P(CCE) across creditors and so on.

FIG. 2 depicts a flow diagram of a method for developing a BusinessCredit Data Interchange (BCDI) score.

At step 210, a BCDI database is obtained or created. That is, the methodoperates to create or obtain a BCDI, as defined above that provides ARinformation where creditor and debtor relationships can be identified.The BCDI database is a compilation of empirically observed historical orarchived creditor and debtor data.

At step 220, an analytic database is created to derive a P(CCE) and/orEUL. That is, using the BDCI database, the method creates an analyticaldatabase that allows the calculation of the P(CCE) and/or EUL. Dataelements are stored in a manner to allow identification of thedebtor/creditor relationship and debtor relationship across allcreditors in a BCDI. Some examples of P(CCE)s and EULs are:

-   -   a) The probability of a debtor obtaining a 91+ day past due        delinquency, write-off, or be placed for collection in the six        month period after scoring;    -   b) The probability of a debtor obtaining a 10% of the        outstanding balance is 120+ day past due delinquency, write-off,        or be placed for collection in the eighteen month period after        scoring;    -   c) The probability of collecting or recovering balances any past        due balances within a three month period after scoring;    -   d) The probability of a write-off within six months of scoring;        and    -   e) The probability of moving to the next aging category the        month after scoring.    -   f) The expected writeoff dollars for a debtor when the CCE is        the occurrence of a 30+ day past due delinquency    -   g) The expected dollar loss for a debtor when the CCE is the        occurrence of a 91+ day past due delinquency and the accounts        are sold to a collection agency at day 210.

At step 230, a bivariate analysis relating various data elements in theBCDI database to the P(CCE) and/or EUL is performed. The bivariateanalysis technique is discussed in more detail below. Specifically, themethod conducts a bivariate statistical analysis upon the AR dataelements found in the BCDI database and may apply transformations tocreate predictive variables. Such transformations entail,illustratively, logs, truncations, censoring, variances, averages,measures of volatilities, compound variables, missing variableassignments and the creation of dichotomous variables among othertransformations and other statistical calculations.

At step 240, a model is developed using a selection technique, and amaximum likelihood estimation (MLE) is applied. That is, the methodfinds the most predictive set of variables using a stepwise, forward,backward or another selection technique.

At step 250, one or more of score class rules, ratings, credit lines andcollection actions are created. That is, the method uses theprobabilities and expected values from the MLE (or other technique) toapply a heuristic or statistical method to group P(CCE) and/or EUL intoScore Classes, Ratings, Credit Lines and/or collection actions.

At step 260, BCDI score is ready for implementation. That is, the methodhas processed the BCDI score such that the algorithm with theempirically derived weights and with Score Class rules, Ratings, CreditLines and/or collection actions can be applied and implemented.

At step 240, a multivariate debtor payment behavior model is developedby processing all candidate variables in association with the dependentvariables according to a selection technique. The set of candidatevariables is expanded to include interactive effects between variables,for example, if A and B are candidate variables then A*B would also beprocessed. That is, the most predictive set of variables is found usinga stepwise selection technique with a complexity penalty, such as theSchwartz Bayes Criterion. The significance level of the selectioncriteria is typically set at the 99% significance level, but the 95%level and other levels can be used with small samples. Alternatively aforward, backward or another selection technique can be used. For eachvariable, the sign of the coefficient is tested against the correlationof that variable with the dependent variable as an additional check forsignificance and also to discourage unnecessary co linearity in themodel.

Also at step 240, a Maximum Likelihood Estimation (MLE) and GeneralLinear Estimation Technique is employed to process the analyticaldatabase provided at step 220. Specifically, an MLE using the LogisticRegression form of the GB2 is used to create the P(CCE) model. It isnoted that the inventors have found that Logistic Regression, as amember of the family of statistical distributions of Generalized Beta ofthe Second Kind (“GB2”), is relatively straightforward to compute, thatthe direction of the estimated parameters can be understood and that thetechnique is well suited for problems with dichotomous dependantvariables, such as made payment versus no payment. However, theinventors also contemplate that other statistical distributions can beused to derive the MLE though these may be computationally burdensomewithout providing any significant increase in predictive power.Furthermore, while other techniques such as Neural Networks and GeneticAlgorithms (among others) can be used, these may disadvantageously makethe function computationally difficult to calculate and, therefore, itmay be difficult to determine the direction of the estimated parameters.Generally, linear regression is not used due to the inherent unequalvariance associated with the error structure of a dichotomous dependentvariable. See the above discussion for Algorithm 1 for more details.

Also at step 240, a Generalized Linear Modeling Technique is employedusing a normal distribution to estimate the EUL Model. Here theestimated P(CCE) is an independent variable along with the CandidateVariables found to be predictive of the EUL. It is noted that theinventors have found that Generalized Linear Modeling, as a member ofthe natural exponential family of statistical distributions, isrelatively straightforward to compute, that the direction of theestimated parameters can be understood and that the technique is wellsuited for problems with positive continuous dependent variables, suchas made payment versus no payment. However, the inventors alsocontemplate that other statistical distributions, such as members of theGB2 family, can be used to derive the MLE though these may becomputationally burdensome without providing any significant increase inpredictive power. Furthermore, while other techniques such as NeuralNetworks and Genetic Algorithms (among others) can be used, these maydisadvantageously make the function computationally difficult tocalculate and, therefore, it may be difficult to determine the directionof the estimated parameters. See the above discussion for Algorithm 2for more details.

An MLE technique, illustratively a Logistic Regression, LinearRegression or some other technique to derive the P(CCE) algorithm ofstep 120 is applied. The inventors have determined that LogisticRegression is relatively easy to compute, the direction of the estimatedparameters can be understood and the technique is well suited forproblems with dichotomous dependant variable. However, other statisticaldistributions can used to derive the MLE, but may be computationallyburdensome without any increase in predictive power. Furthermore, othertechniques such as Neural Networks and Genetic Algorithms (among others)may be used, though such techniques may make the functioncomputationally difficult to calculate and make the determination of thedirection of the estimated parameters difficult. Generally, linearregression is not used due to the inherent unequal variance associatedwith a dichotomous dependent variable.

Bivariate Analysis

The invention uses a sample of historical debtor data, creditor data,debtor/creditor data and/or other data to be used as independentvariables to develop a model that predicts the P(CCE) (e.g., 30 daydelinquency) and EUL (e.g., expected writeoff dollars). The model isthen stored in memory for subsequent use in processing current debtordata, creditor data, debtor/creditor data and/or other data to predictcurrent debtor behavior. The model may be debtor or account typespecific to increase the correlation between the historic data drivenmodel and the current data prediction.

Specifically, the invention creates a set of variables for bivariateanalysis (“analysis variables”) for a debtor payment behavior model froma set of historical data elements that are old enough to observe thedependent variable. The data elements fall into two broad types:numerical and categorical. Some data elements may be considered amixture of these types and hence are analyzed using a mixture of themethodologies described herein. If a categorical data element has anycategories with a numerical value, an additional data element isconstructed by treating each numerical category as a number and theother categories as missing values. The date of observation is always adata element.

The analysis variable creation process is performed, illustratively,four times, since conducting all of the desired transformations andmathematical operations in one processing step may be computationallyburdensome; however, it may be desirable in some environments to do thisin one step.

The data elements for the first iteration of the analysis variablecreation process come from the data request(s) shown later in thispatent application. The analysis variables created from the firstiteration through the set of data elements become the data elements forthe second iteration, etc. Analysis variables that would first arisefrom the last iteration (illustratively the fourth iteration) are“frontier variables”. If any candidate variables (described below) basedupon frontier variables make the model cut described in step 240 thenthe entire process will be repeated for a fifth time and a new set offrontier variables will be defined. Additional repeats may be necessaryuntil the process ceases to generate variables that make the model cutusing a complexity criteria described below

Every data element becomes an analysis variable. Additional analysisvariables are created from numerical or categorical data elements andmay involve transformations such as logarithms and exponentiation aswell as other mathematical transformations. Another type oftransformation involves the breakdown of a data element into componentanalysis variables. For example, the Date of first lease/loan acrosscreditors creates three component variables: year of first lease/loan,month of first lease/loan, and day of first lease/loan. Yet another typeof transformation involves categorization of a data element based uponadditional information and/or databases. For example, the SIC code for acommercial debtor would be compared with a list of SIC industry groupsto create an analysis variable that indicates membership in certaingroups.

Analysis variables may also be based upon relationships among dataelements. Mathematical operations, such as addition, subtraction,multiplication, division, equalities, and inequalities are used togenerate new analysis variables from each pair of numerical dataelements. Equalities and inequalities can be applied to pairs ofcategorical data elements as well as mixed pairs.

Analysis variables are also created from groups of data elements.Mathematical functions such as sums, variances, averages and measures ofvolatilities as well as other statistical calculations are applied togroups of numerical data elements. For each group a family of countvariables is constructed by counting the number of instances aparticular value occurs in that group. Count variables can beconstructed for groups of categorical data elements.

When available, a time series may be constructed and analysis variablescreated from its elements using variances, averages and measures ofvolatilities as well as other statistical calculations. The time serieswould also be considered as a group (described above).

After the set of analysis variables has been created, the inventioncreates variables for use as candidates in step 240 (“candidatevariables”). All candidate variables must take only numerical values.Every numerical analysis variable generates one, possibly several,candidate variables. Missing values will be assigned a numeric valueusing the average of the variable for non missing values. Additionaltechniques for missing data include an inversion of the linearregression line for the dependent variable verses the analysis variable,where the inversion is calculated for the average of the dependent whenthe analysis variable is missing. An alternate technique comprises animputation of value based upon statistical relationships of the analysisvariable to other analysis variable(s), for example the assignment of amissing writeoff date as 180 days after a known last payment date.

Truncation and censorship are optionally used to treat outliers asmissing values (described above) and/or replace extreme values with lessextreme values. The invention uses percentile steps of, illustratively,1%, 2%, 3%, 4%, 5%, etc. and 99%, 98%, 97%, 96% etc. to determineapplicable cutoffs, though other steps may be used.

For a numerical analysis variable, one embodiment of the invention usesa method of maximum likelihood to create additional candidate variablesfrom the creation of categories. The creation of categories is a step inthe model building process for both of the algorithms described above.

For a continuous numerical analysis variable, the invention partitionsvariables into ordered categories of equal size. Within the context ofthe present invention, 100 ordered categories are usually sufficient,although a finer partition (i.e., more categories) is optionally usedwhere data of sufficient volume is present. Missing values, if present,form a distinct category outside the 100 ordered categories and will beconsidered in the last step of the method.

For a discrete numerical analysis variable the process is similar,except that the “lumpiness” of the discrete variable may prevent theformation of 100 groups. For example, a discrete variable that has onlythree possible values would have only three possible categories.

For a categorical analysis variable with an a priori ordering theprocess is similar to that of a discrete numerical analysis variable.Furthermore, this variable is also analyzed as a variable without apriori ordering (as described further below).

For a categorical analysis variable that does not have an a prioriordering, such as a state of address, the invention orders thecategories by the average value of the dependent variable in thosecategories.

For each N (N=1 to 100), the invention creates substantially allpossible ordered groupings of the ordered categories. For example, ifN=1, the group is the entire dataset. If N=2, the first possiblegrouping contains category 1 as group1 and category 2 thru 100 asgroup2. The second possible grouping contains category 1, 2 as group1and category 3 thru 100 as group2. There are 99 possible groupingsbecause the invention enforces a rule that the categories of a groupmust be adjacent to each other in the ordering. For N=3 there are98*99/2 possible groupings, etc.

For N=1, the invention evaluates the maximum likelihood of a model thatassigns the dependent variable average to all observations (exceptobservations with missing values). For N=2 the invention evaluates themaximum likelihood of all 99 possible groupings of a model that assignsthe dependent variable average for a group to all observations in thatgroup. The “best” grouping is found. By mathematical necessity the N=2statistic will improve upon the N=1 statistic. The maximum likelihood iscalculated in accordance with the model. For example, the bivariateanalysis when the dependent variable is dichotomous will typically uselogistic regression.

The invention then compares the maximum likelihood for N=1 to themaximum likelihood for the best N=2 group. The N=2 statistic ispenalized using, illustratively, the Schwartz-Bayes Criteria to see ifthe improvement is statistically significant. Alternative statisticalpenalties include the Akaike Information Criterion. If it is, then theinvention will discard N=1 and will use the N=2 groupings to createcategories.

The process then generates candidate variables that cover the createdgroups by using 0/1 indicators. In general N groups will create N−1candidate variables. For example, if N=3 groups have been determined, 2candidate variables will be created. The first candidate variable hasthe value 1 for group 1 and 0 for groups 2 and 3. The second candidatevariable has the value 1 for groups 1 and 2 and 0 for group 3. If adistinct missing values group is present then that group may be assigned0 or 1, depending on which assignment creates the more predictivecandidate variable as measured by the likelihood statistic of thecorresponding 1 variable model. There would also be a candidate variablethat will have the value 0 for all groups with non missing values and 1for the distinct missing group.

Continuous candidate variables with a wide range may be truncated frombelow (left) or above (right) of the distribution in order to improvethe likelihood statistics of the variable.

Thus a set of candidate variables that take only numerical values willhave been created that will be used to build the models for P(CCE)and/or EUL. Some of these candidate variables will be designatedfrontier variables whose presence in a model developed in step 240 maynecessitate more iterations of the analysis variable creation processand a possible repeat of the model development process. The goal is tocreate a sufficiently large set of variables so that subsequentiterations of the analysis variable creation process would not generatenew predictive variables that would significantly improve the model.

It is noted that the term “payment” as used herein is intended to begenerally synonymous with the term “monetary.”

FIG. 3 depicts a flow diagram of a method for implementing a BCDI score.Specifically, FIG. 3 depicts an implementation where the algorithm issent to a computer hosting the BCDI. As will be discussed below, FIG. 3depicts an implementation where the algorithm is sent to the computer ofthe algorithm holder.

At step 310, the P(CCE) and/or EUL algorithm formula is applied to acomputer where the BCDI resides. That is, the method creates a paneledanalytical data base (or uses a relational data base) that will allowthe application of the P(CCE) and/or EUL formula and the development ofScore Classes Ratings, Credit Lines and/or collection actions. Thisinput data can be housed on site or can be delivered through theInternet or another type of networking.

At step 320, a creditor makes an inquiry for the BCDI report and scores.That is, with respect to production and delivery of the BCDI product, aCreditor makes an inquiry for a Business Credit Data Interchange Reportvia account number, debtor name and address or other unique key.

At step 330, SQL software or other query language software creates tradeinterchange reports containing P(CCE) and/or EUL score class rules,ratings, credit lines and/or collection actions and the like. That is,based upon the inquiry of step 320, SQL software or another querylanguage will create the Trade Interchange Reports that include theP(CCE) and/or EUL formula, Score Class and Ratings that compare thedebtor/creditor relationship with the debtor/BCDI relationship withrespect to credit and collections.

At step 340, a report is delivered to the requesting creditor via theInternet or other electronic or non-electronic means. That is, in oneembodiment the report is delivered back to the creditor via the Internetin HTTP, SHTTP, FTP, SFTP or e-mail formats, while in other embodimentsa paper report may be sent via mail.

FIG. 4 depicts a flow diagram of an alternate method for implementing aBCDI score. Specifically, FIG. 4 depicts an implementation where thealgorithm is sent to the computer of the algorithm holder.

At step 410, a copy of the BCDI is sent to a computer system associatedwith the algorithm holder.

At step 420, P(CCE) and/or EUL algorithm is applied to the receivingcomputer.

At step 430, the score class rules, ratings, credit lines and/orcollection actions and PAR are sent back to the holder of the BCDI viathe Internet or other electronic or non-electronic transfer means.

At step 440, a creditor makes an inquiry for BCDI report and scores.

At step 450, SQL software or other query language software creates tradeinterchange reports containing P(CCE) and/or EUL score class rules,ratings, credit lines and/or collection actions and the like.

At step 460, a report is delivered to the requesting creditor via theInternet or other electronic or non-electronic means.

Generally speaking, the present invention is adapted to provide creditand collection managers, in companies that offer trade, leasing,installment and other types of credit, the ability to compare the odds,or probability based score, of the CCE and/or EUL occurring for a givencustomer or debtor to the probability and/or financial consequence ofthis same event occurring with respect to other creditors that are doingbusiness with the same customer or debtor. This information is useful toa creditor for determining whether the customer or debtor will pay onecreditor better than, worse than or about the same as the customer ordebtor's other creditors. The credit and collection managers of thecreditor company can also use this information to develop and implementstrategies for extending further credit and/or to ensure timely payment.

By utilizing the present invention, a creditor may compute comparisonsof the probabilities of payment and/or EUL to determine where thecreditor ranks within a hierarchy of payments associated with aparticular debtor. The position of a particular creditor within thehierarchy of creditors associated with a particular debtor is indicativeof the urgency or likelihood of the debtor to pay the particularcreditor vis-à-vis the other creditors.

For example, within a commercial context a major company that a givencustomer must have as a supplier may not be aware that the customer ishaving problems paying bills to other companies because the majorcompany is always paid on a timely basis, right up until the time thecustomer cannot pay anyone anymore. That is, since the major company isa critical supplier, the customer of the major company cannot risk a cutoff of supply and, therefore, appropriately pays the major companyuntil; for example, the customer goes out of business.

FIG. 5 depicts a high-level block diagram of a general-purpose computersuitable for use in performing any of the functions described herein. Asdepicted in FIG. 5, system 500 comprises a processor element 502 (e.g.,a CPU), a memory 504, e.g., random access memory (RAM) and/or read onlymemory (ROM), a performance monitoring module 405, and variousinput/output devices 406 (e.g., storage devices, including but notlimited to, a tape drive, a floppy drive, an optical disk drive, harddisk drive or a compact disk drive, a receiver, a transmitter, aspeaker, a display, an output port, and a user input device such as akeyboard, a keypad, a mouse, and the like).

It should be noted that the present invention may be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a general purposecomputer or any other hardware equivalents. In one embodiment, thepresent performance monitoring process 405 can be loaded into memory 404and executed by processor 402 to implement the functions as discussedabove. As such, performance monitoring process 405 (including associateddata structures) of the present invention can be stored on a computerreadable medium or carrier, e.g., RAM memory, magnetic or optical driveor diskette and the like.

It is contemplated that some of the steps discussed herein as softwaremethods may be implemented within hardware, for example, as circuitrythat cooperates with the processor to perform various method steps.Portions of the present invention may be implemented as a computerprogram product wherein computer instructions, when processed by acomputer, adapt the operation of the computer such that the methodsand/or techniques of the present invention are invoked or otherwiseprovided. Instructions for invoking the inventive methods may be storedin fixed or removable media, transmitted via a data stream in abroadcast or other signal bearing medium, and/or stored within a workingmemory or mass storage associated with a computing device operatingaccording to the instructions.

1. A non-transitory computer readable medium containing a program which,when executed by a processor, performs a method comprising: aggregatingat least one of account receivable data and payment data of a debtor (i)from each of a plurality of creditors (j); and using the aggregateddebtor data and debtor data from the plurality of the creditors (j)determining a probability of a credit or collection event (P(CCE_(ij)))of the debtor (i) with respect to at least one creditor of the pluralityof the creditors (j); storing, in a memory, values corresponding to the(P(CCE_(ij))), wherein the P(CCE_(ij)) is determined according to thefollowing model: P(CCE_(ij)) is a function of (AR_(ij), APP_(ij),OTHER_(i), BCDI_(i1), BCDI_(i2) . . . BCDI_(iJ)); where (AR_(ij))comprises accounts receivable data for debtor (i) with creditor (j);(APP_(ij)) comprises optional internal data for debtor (i) with creditor(j); (OTHER_(j)), comprises optional third party data on debtor (i)selected from at least one of a credit bureau, a demographic data sourceor a private data source; and (BCDI_(ij)) comprises Business Credit DataInterchange data elements for debtor (i) across all J creditors in theBCDI; and making a credit decision utilizing the stored valuescorresponding to the (P(CCE_(ij))).
 2. A non-transitory computerreadable medium containing a program which, when executed by aprocessor, performs a method comprising: adapting a debtor score at afirst creditor model in response to account receivable data and paymentdata of the debtor received from a second creditor model, each of thecreditor models being generated according to respective Business CreditData Interchange (BCDI) group, wherein the debtor score comprises aprobability of a credit or collection event (P(CCE_(ij))) of the debtor(i) with respect to at least one creditor of a plurality of thecreditors (j), wherein P(CCE_(ij)) is a function of AR_(ij), APP_(ij),OTHER_(j), BCDI_(j1), BCDI_(j2) . . . , BCDI_(ij): where AR comprisesaccounts receivable data for debtor (i) with creditor (APP_(ij))comprises optional internal data for debtor (i) with creditor (j);(OTHER_(j)) comprises optional third party data on debtor (i) selectedfrom at least one of a credit bureau, a demographic data source or aprivate data source; and (BCDI_(ij)) comprises Business Credit DataInterchange data elements for debtor (i) across all J creditors in theBCDI; storing, in a memory, values corresponding to the debtor score;and making a credit decision utilizing the stored values correspondingto the (P(CCE_(ij))).
 3. The non-transitory computer readable claim 1,further comprising: calculating an expected utility loss (EUL) of adebtor with respect to at least one creditor according to the followingequation:EULij=P(CCEij)*ULij; where utility loss (UL) is a function of one ormore of balance outstanding and collection effort costs.
 4. Thenon-transitory computer readable f claim 1, further comprising:receiving a creditor inquiry regarding a particular debtor; andevaluating expected behavior of the particular debtor with respect tothe inquiring creditor according to the P(CCE) data.
 5. Thenon-transitory computer readable claim 4, further comprising: providingto the inquiring creditor a report regarding the evaluation of therespective debtor.
 6. The non-transitory computer readable claim 2,further comprising: generating the account receivable data by performinga bivariate statistical analysis upon accounts receivable data elementswithin the BCDI group.
 7. The non-transitory computer readable claim 6,further comprising: applying one or more transformations to the accountreceivable data to create predictive variables, the one or moretransformations comprising any of logs, truncations, censoring,variances, averages, measures of volatilities, compound variables,missing variable assignments and the creation of dichotomous variables.8. The non-transitory computer readable claim 7, wherein: said firstcreditor model is generated by processing candidate predictive variablesin association with the dependent variables according to a selectiontechnique.
 9. The non-transitory computer readable f claim 8, whereinthe selection technique comprises a stepwise selection technique havingassociated with it a complexity penalty.
 10. The non-transitory computerreadable claim 9, wherein the complexity penalty comprises a SchwartzBayes Criterion.
 11. The non-transitory computer readable claim 1,wherein the account receivable data further comprises amount 1-30 dayspast due across creditors.
 12. The non-transitory computer readableclaim 1, wherein the account receivable data further comprises totaloutstanding balance across creditors, amount past due across creditors,amount of write-off (loss) across creditors, amount placed forcollection across creditors, and average days past due across creditors.13. The non-transitory computer readable claim 2, wherein the respectiveBCDI groups includes data relating to an amount 1-30 days past dueacross creditors.
 14. The non-transitory computer readable claim 2,wherein the respective BCDI groups includes further comprises datarelating to total outstanding balance across creditors, amount past dueacross creditors, amount of write-off (loss) across creditors, amountplaced for collection across creditors, and average days past due acrosscreditors.